Computer Science > Computation and Language
arXiv:2306.16638 (cs)
[Submitted on 29 Jun 2023]
Title:A negation detection assessment of GPTs: analysis with the xNot360 dataset
View a PDF of the paper titled A negation detection assessment of GPTs: analysis with the xNot360 dataset, by Ha Thanh Nguyen and 4 other authors
View PDFAbstract:Negation is a fundamental aspect of natural language, playing a critical role in communication and comprehension. Our study assesses the negation detection performance of Generative Pre-trained Transformer (GPT) models, specifically GPT-2, GPT-3, GPT-3.5, and GPT-4. We focus on the identification of negation in natural language using a zero-shot prediction approach applied to our custom xNot360 dataset. Our approach examines sentence pairs labeled to indicate whether the second sentence negates the first. Our findings expose a considerable performance disparity among the GPT models, with GPT-4 surpassing its counterparts and GPT-3.5 displaying a marked performance reduction. The overall proficiency of the GPT models in negation detection remains relatively modest, indicating that this task pushes the boundaries of their natural language understanding capabilities. We not only highlight the constraints of GPT models in handling negation but also emphasize the importance of logical reliability in high-stakes domains such as healthcare, science, and law.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2306.16638 [cs.CL] |
| (orarXiv:2306.16638v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2306.16638 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled A negation detection assessment of GPTs: analysis with the xNot360 dataset, by Ha Thanh Nguyen and 4 other authors
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